scholarly journals A class of ratio-type estimators under two-phase sampling in the presence of auxilary variables

2019 ◽  
Vol 53 (1) ◽  
pp. 79-91
Author(s):  
P. A. Patel ◽  
F. H. Shah
Keyword(s):  
Finite Population ◽  
Natural Populations ◽  
Small Scale ◽  
Two Phase ◽  
Population Mean ◽  
First Order ◽  
Special Cases ◽  
Phase Sampling ◽  
Class Of Estimators ◽  
Two Phase Sampling

This paper deals with the estimation of population mean under two-phase sampling. Utilizing information on two-auxiliary variables, a class of estimators for estimating the finite population mean is proposed, and its properties, up to the first order of approximation, are studied. Various estimators are suggested as special cases of this class. The performance of the suggested estimators is compared with some contemporary estimators of population mean through numerical illustrations carried over existing datasets of some natural populations. Also, a small scale Monte Carlo simulation is carried out for the empirical comparison.

scholarly journals A general class of estimators on estimating population mean using the auxiliary proportions under simple and two phase sampling

2021 ◽  
Vol 6 (12) ◽  
pp. 13592-13607
Author(s):  
Xuechen Liu ◽  
◽  
Muhammad Arslan ◽  
Keyword(s):  
Finite Population ◽  
Two Phase ◽  
Population Mean ◽  
Mean Squared Errors ◽  
First Order ◽  
Mathematical Expressions ◽  
Phase Sampling ◽  
Class Of Estimators ◽  
The Mean ◽  
Two Phase Sampling

<abstract><p>This article deals with estimation of finite population mean using the auxiliary proportion under simple and two phase sampling scheme utilizing two auxiliary variables. Mathematical expressions for the mean squared errors of the proposed estimators are derived under first order of approximation. We compare the proposed class of estimators "theoretically and numerically" with the usual mean estimator of Naik and Gupta <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup>. The theoretical as well as numerical findings support the superiority of our proposed class of estimator as compared to estimators available in literature.</p></abstract>

A Class of Estimators for a Finite Population Mean Based on Multivariate Information and General two Phase Sampling

1995 ◽  
Vol 45 (3-4) ◽  
pp. 203-218 ◽  
Author(s):  
T. P. Tripathi ◽  
M. S. Ahmed
Keyword(s):  
Finite Population ◽  
Population Data ◽  
Two Phase ◽  
Population Means ◽  
Population Mean ◽  
Phase Sampling ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Relative Gains ◽  
Two Phase Sampling

A class of estimators for a finite population mean is presented for the situations where population means of some auxiliary variables are known while those of others are unknown. The results for general two phase sampling are indicated while the detailed discussion is made for the case when SRSWOR is used at both the phases. While several known estimators belong to the proposed clas~ some new estimators are identified as well. The optimum estimator in the proposed class is found to be better than the so-called chain ratio and regression estimators discu ssed by Chand (1975). Kiregyera (1984) and Mukerjee et al. (1987). The relative gains in efficiency of tho proposed optimum estimator over the others are obtained for a natural population data and found to be quite appreciable.

scholarly journals An Improvement in Estimation of Population Mean using Two Auxiliary Variables and Two- Phase Sampling Scheme under Non-Response

2021 ◽  
Author(s):  
Manoj K. Chaudhary ◽  
Amit Kumar
Keyword(s):  
Finite Population ◽  
Real Data ◽  
Sampling Scheme ◽  
Auxiliary Variables ◽  
Two Phase ◽  
Data Set ◽  
Population Mean ◽  
First Order ◽  
Phase Sampling ◽  
Two Phase Sampling

In the present paper, we have proposed some improved ratio and regression-type estimators of the finite population mean utilizing the information on two auxiliary variables in the presence of non-response. The two-phase sampling scheme has been used to accomplish the job of estimating the desired parameter. The expressions for the basic properties such as bias and mean square error (MSE) of the proposed estimators have been derived up to the first order of approximation. A comparative study of the proposed estimators with some existing estimators has also been carried out through a real data set.

scholarly journals A Study on the Chain Ratio-Type Estimator of Finite Population Variance

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yunusa Olufadi ◽  
Cem Kadilar
Keyword(s):  
Finite Population ◽  
Numerical Comparison ◽  
Mean Square ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Two Phase ◽  
First Order ◽  
Phase Sampling ◽  
The Mean ◽  
Two Phase Sampling

We suggest an estimator using two auxiliary variables for the estimation of the unknown population variance. The bias and the mean square error of the proposed estimator are obtained to the first order of approximations. In addition, the problem is extended to two-phase sampling scheme. After theoretical comparisons, as an illustration, a numerical comparison is carried out to examine the performance of the suggested estimator with several estimators.

scholarly journals Some Exponential Type Predictive Estimators of Finite Population Mean in Two-Phase Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 51-57
Author(s):  
Asifa Kamal ◽  
Nimra Amir ◽  
Huma Dastagir
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Numerical Study ◽  
Real Life ◽  
Data Sets ◽  
Two Phase ◽  
Population Mean ◽  
Real Life Data ◽  
Phase Sampling ◽  
Two Phase Sampling

This study is designed for predictive estimation of finite population mean in two phase sampling using two auxiliary variables. Two phase exponential ratio type estimator and exponential chain ratio type estimator are proposed under predictive approach suggested by Bahl & Tuteja (1991). The expressions of bias and mean square error of both suggested estimators have been carried out for theoretical comparison. The numerical study on real life data sets as well as simulation study has been conducted to examine the performance of suggested estimators. Finally, it is demonstrated that the suggested exponential ratio estimators are more efficient than competitive estimators in support of numerical and simulation study as well.

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